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相关概念视频

Physiological Pharmacokinetic Models: Blood Flow-Limited Versus Diffusion-Limited Models00:57

Physiological Pharmacokinetic Models: Blood Flow-Limited Versus Diffusion-Limited Models

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Physiological pharmacokinetic models, often called flow-limited or perfusion models, typically assume a swift drug distribution between tissue and venous blood, creating a rapid drug equilibrium. This premise is based on the idea that drug diffusion is extremely fast, and the cell membrane presents no barrier to drug permeation. In this scenario, where no drug binding occurs, the drug concentration in the tissue equals that of the venous blood leaving the tissue. This greatly simplifies the...
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Diffusion01:12

Diffusion

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Diffusion is the passive movement of substances down their concentration gradients—requiring no expenditure of cellular energy. Substances, such as molecules or ions, diffuse from an area of high concentration to an area of low concentration in the cytosol or across membranes. Eventually, the concentration will even out, with the substance moving randomly but causing no net change in concentration. Such a state is called dynamic equilibrium, which is essential for maintaining overall...
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Theories of Dissolution: The Danckwerts' Model and Interfacial Barrier Model01:09

Theories of Dissolution: The Danckwerts' Model and Interfacial Barrier Model

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Various dissolution theories provide insight into the factors that influence the dissolution rate. Danckwerts' Model suggests that turbulence, rather than a stagnant layer, characterizes the dissolution medium at the solid-liquid interface. In this model, the agitated solvent contains macroscopic packets that move to the interface via eddy currents, facilitating the absorption and delivery of the drug to the bulk solution. The regular replenishment of solvent packets maintains the...
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Passive Diffusion: Overview and Kinetics01:17

Passive Diffusion: Overview and Kinetics

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Passive diffusion is a critical process that allows small lipophilic drugs to cross the cell membrane along a concentration gradient. This mechanism's efficiency depends on four primary factors: the membrane's surface area, the drug's lipid-water partition coefficient, the concentration gradient, and the membrane's thickness.
When administered orally, drugs establish a substantial concentration gradient between the gastrointestinal (GI) lumen and the bloodstream, expediting...
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Van der Waals Interactions01:24

Van der Waals Interactions

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Atoms and molecules interact with each other through intermolecular forces. These electrostatic forces arise from attractive or repulsive interactions between particles with permanent, partial, or temporary charges. The intermolecular forces between neutral atoms and molecules are ion–dipole, dipole–dipole, and dispersion forces, collectively known as van der Waals forces.
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Theories of Dissolution: Diffusion Layer Model01:15

Theories of Dissolution: Diffusion Layer Model

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Dissolution, the process by which drug particles dissolve in a solvent, is explained by the diffusion layer model, a theoretical framework that simulates the absorption of oral drugs and allows us to analyze experimental data.
This process starts with a thin layer, saturated with the drug, forming at the interface between the solid and liquid. The solute then diffuses from this layer into the main solution. The Noyes-Whitney equation suggests that the rate of dissolution relies on the diffusion...
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Synthesis of Cyclic Polymers and Characterization of Their Diffusive Motion in the Melt State at the Single Molecule Level
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扩散模型作为强大的对手

Xuelong Dai, Yanjie Li, Mingxing Duan

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    此摘要是机器生成的。

    本研究探讨了扩散模型作为对抗性威胁,开发了一种使用仅生成数据的新型无盒攻击方法. 研究表明,有效的对抗性攻击不需要原始训练数据.

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    科学领域:

    • 人工智能的人工智能
    • 计算机视觉 计算机视觉
    • 机器学习 机器学习

    背景情况:

    • 扩散模型擅长生成高质量的图像.
    • 它们的对抗能力,特别是在无框攻击场景中,仍然未被充分探索.
    • 现有的无框攻击通常需要访问训练数据,限制现实世界的适用性.

    研究的目的:

    • 调查扩散模型的对抗潜力.
    • 提出一种新的无框对抗性攻击方法,仅使用扩散模型生成的数据.
    • 为了评估攻击对ImageNet数据集的有效性.

    主要方法:

    • 使用扩散模型来训练替代模型生成合成数据集.
    • 使用分类扩散模型微调替代模型,结合模型不确定性和噪声增强.
    • 通过通过多个推断的替代模型的平均近似来对取样的对抗性示例.

    主要成果:

    • 拟议的方法在无框对抗性攻击中实现了最先进的性能.
    • 证明成功的对抗性攻击,仅使用扩散模型生成的数据.
    • 在具有挑战性的ImageNet数据集上验证了有效性.

    结论:

    • 扩散模型可以有效地用于复杂的对抗性攻击.
    • 开发的无框攻击方法为现实世界的威胁场景提供了实际的解决方案.
    • 这项研究强调了扩散模型作为强大的生成工具和潜在的安全风险的双重性质.